# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import os
import unittest
from functools import partial

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertBmmTest_dynamic(TrtLayerAutoScanTest):
    def sample_program_configs(self):
        def generate_input(shape):
            return np.random.random(shape).astype(np.float32)

        for batch in [10, 11, 12, 13, 14, 15]:
            for trans_x in [False]:
                for trans_y in [False]:
                    input1_shape = [batch, 350, 75]
                    input2_shape = [batch, 75, 25]
                    dics = [{}]
                    ops_config = [
                        {
                            "op_type": "bmm",
                            "op_inputs": {
                                "X": ["input1_data"],
                                "Y": ["input2_data"],
                            },
                            "op_outputs": {"Out": ["output_data"]},
                            "op_attrs": dics[0],
                        }
                    ]
                    ops = self.generate_op_config(ops_config)

                    program_config = ProgramConfig(
                        ops=ops,
                        weights={},
                        inputs={
                            "input2_data": TensorConfig(
                                data_gen=partial(generate_input, input2_shape)
                            ),
                            "input1_data": TensorConfig(
                                data_gen=partial(generate_input, input1_shape)
                            ),
                        },
                        outputs=["output_data"],
                    )

                    yield program_config

    def generate_dynamic_shape(self, attrs):
        self.dynamic_shape.min_input_shape = {
            "input1_data": [10, 350, 75],
            "input2_data": [10, 75, 25],
        }
        self.dynamic_shape.max_input_shape = {
            "input1_data": [100, 350, 75],
            "input2_data": [100, 75, 25],
        }
        self.dynamic_shape.opt_input_shape = {
            "input1_data": [15, 350, 75],
            "input2_data": [15, 75, 25],
        }
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> tuple[paddle_infer.Config, list[int], float]:
        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if dynamic_shape:
                return 1, 3
            else:
                return 0, 4

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        clear_dynamic_shape()
        if not run_pir:
            self.trt_param.precision = paddle_infer.PrecisionType.Float32
            program_config.set_input_type(np.float32)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-5,
            )
            self.trt_param.precision = paddle_infer.PrecisionType.Half
            program_config.set_input_type(np.float16)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                (1e-2, 1e-2),
            )

        # The output has little diff between gpu and trt in CI-Windows-Inference
        tol_fp32 = 1e-4
        tol_half = 1e-2
        if os.name == 'nt':
            tol_fp32 = 1e-2
            tol_half = 1e-2
        # for dynamic_shape
        self.generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            tol_fp32,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            (tol_half, tol_half),
        )

    def add_skip_trt_case(self):
        pass

    def test(self):
        self.add_skip_trt_case()
        self.run_test(run_pir=True)


if __name__ == "__main__":
    unittest.main()
